“AI inside's Takuji Tokuchi anticipates 2026 being the year of 'task-completing AI' as the challenges of time and responsibility are overcome.”

“AI's memory is divided into two main phases...”

“We launched ChatGPT as a research preview to understand what would happen if we put frontier intelligence directly in people's hands.”

“By 2026, over 80% of companies are predicted to deploy generative AI applications.”

“ChatGPT can now remember conversations from a year ago, and link you directly to them.”

“Newelle, AI assistant for Linux, has been updated to 1.2!”

“I programmed it so most tools when called simply make API calls to separate agents. Having agents run separately greatly improves development and improvement on the fly.”

“Suppliers of key components are temporarily halting production.”

“The PreCompact hook automatically backs up your context before compression occurs.”

“I built this 3D sim to visualize how a 1D-CNN processes time-series data (the yellow box is the kernel sliding across time).”

“The report highlights key advancements in the AI sector.”

“3 Pro responded it is yoghurt with granola, and commented it was hidden in the biography of a character of the roleplay.”

““This episode reflects on the accuracy of our previous predictions and uses that assessment to inform our perspective on what’s ahead for 2026.” (Hypothetical Quote)”

“Is this actually possible, or would the sentences just be generated on the spot?”

“INSTRUCTIONS: 1. "title_en", "title_jp", "title_zh": Professional, engaging headlines.”

“Lots of companies have also deprecated their internally built solution to switch over, dealing with GPU infra and onboarding docker containers not being a very exciting problem when the company you work for is trying to cure cancer.”

“In an industry in constant flux, sticking your neck out to predict what’s coming next may seem reckless.”

“There is a lot of debate about AI for mental health.”

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“Claims were made that we were on the verge of pinnacle AI. Not yet.”

“奥特曼正骑着独轮车，手里抛接着越来越多的球 (Altman is riding a unicycle, juggling more and more balls).”

“Get lifetime access to 1min.AI’s multi-model AI hub for just $74.97 (reg. $540) — no monthly fees, ever.”

“Last January, I posted "3 predictions for what will happen in the LLM (Large Language Model) industry in 2025," and thanks to you, many people viewed it.”

“Assuming the article's premise is correct: "The rapid expansion of AI capabilities is being fueled by unprecedented levels of debt, creating a precarious financial situation."”

“OpenForecaster 8B matches much larger proprietary models, with our training improving the accuracy, calibration, and consistency of predictions.”

“PRISM addresses the challenge through a learnable tree-based partitioning of the signal.”

“The authors find that the background noise at 100 μHz is dominated by drift with a power law of 1/f^2, accompanied by a few dominant two-level fluctuators and an average linear correlation length of (188 ± 38) nm in the device.”

“ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision.”

“The exttt{Mgformer}-based module is superior in performance and flexibility. Its representative recall and precision values are 0.79 and 0.76, respectively, and can be modified by adjusting the threshold.”

“DeepSeek broke the scaling thesis. Anthropic won coding. China dominated open source.”

“The resulting observable is mapped into a transparent decision functional and evaluated through realized cumulative returns and turnover.”

“The combined SPHEREx + 7DS dataset significantly improves redshift estimation compared to using either the SPHEREx or 7DS datasets alone, highlighting the synergy between the two surveys.”

“Compared to existing state-of-the-art AI models, our system offers higher spatial resolution. It is cheap to train/run and requires no additional post-processing.”

“USF-MAE achieved the highest performance across all evaluation metrics, with 90.57% accuracy, 91.15% precision, 90.57% recall, and 90.71% F1-score.”

“The paper introduces a general, model-agnostic training and inference framework for joint generative forecasting and shows how it enables assessment of forecast robustness and reliability using three complementary uncertainty quantification metrics.”

“We uncover distinct features on a wide range of length and time scales that correspond to tropical cyclones, atmospheric rivers, diurnal and seasonal behavior, large-scale precipitation patterns, specific geographical coding, and sea-ice extent, among others.”

“Novice endoscopists exposed to EndoRare-generated cases achieved a 0.400 increase in recall and a 0.267 increase in precision.”

“CoHalLo achieves a Top-1 accuracy of 0.4253, Top-3 accuracy of 0.6149, Top-5 accuracy of 0.7356, Top-10 accuracy of 0.8333, IFA of 5.73, Recall@1% Effort of 0.052721, and Effort@20% Recall of 0.155269, which outperforms the baseline methods.”

“Nearly all evaluated jailbreak techniques can be detected by at least one safety filter.”

“The SPDE-based extensions improve both point and probabilistic forecasts relative to standard benchmarks.”

“The multimodal Transformer achieves RMSE = 0.90 mm and R^2 = 0.97 on the test set on the eastern Ireland tile (E32N34).”

“The Transformer achieved the highest predictive accuracy with an R^2 of 0.9696.”

“A positive correlation between LAP and forecast accuracy indicates the presence and magnitude of lookahead bias.”

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“Responses to different perturbation types are dynamically incompatible when they occur in separate experiments... On the other hand, if both perturbation types are presented at random during the same experiment then the responses are compatible with each other and can be construed as produced by a unique underlying mechanism.”

“SLMs struggle to follow the required style when the instruction is placed in system messages rather than user messages, which contradicts the intended function of system prompts.”

“ACT achieves an F1 score of 0.91, with superior Recall (0.89) and Precision (0.94).”

“Incorporating scale gap metadata substantially improved the predictive performance of LLMs, with Gemini Stage 2 achieving the highest accuracy, with a mean absolute error of 5.43 cm, root mean square error of 8.58 cm, and R squared of 0.84 under optimal image conditions.”

“The paper finds that the value of priority access is discounted relative to risk-neutral valuation due to the difficulty of forecasting short-horizon volatility and bidders' risk aversion.”

“CoLog achieves a mean precision of 99.63%, a mean recall of 99.59%, and a mean F1 score of 99.61% across seven benchmark datasets.”